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Record W4407615798 · doi:10.1561/108.00000068

Some Fallacies in Corporate Finance: A Coaseian Perspective

2025· article· en· W4407615798 on OpenAlexaff
Varouj A. Aivazian, Jeffrey L. Callen

Bibliographic record

VenueJournal of Law Finance and Accounting · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsPerspective (graphical)Corporate financeEconomicsAccountingBusinessFinanceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We argue that the Modigliani and Miller (1958, 1961, 1963) Irrelevance Theorems are subsumed by the Coase Theorem (Coase, 1960). We employ the Coase Theorem to critique two fundamental results in the corporate finance literature. Specifically, we reject the claim by DeAngelo and DeAngelo (2006) that dividends are relevant in frictionless markets. Using the logic of the Coase Theorem, we argue that the solution offered by DeAngelo and DeAngelo (2006) is not in equilibrium. In addition, we reject the claim by Myers (1977) that corporate investment is negatively related to leverage in frictionless markets (the so-called underinvestment problem). If the firm plans to underinvest because of debt overhang, shareholders and debt holders will costlessly re-contract around the debt overhang until the firm takes on the optimal investment. However, if we were to interpret Myers (1977) as implicitly assuming transaction costs or other frictions, then an underinvestment equilibrium could emerge since Coaseian efficiency generally fails to emerge in such settings. In the context of Myers (1977), transaction costs of re-contracting limit the full internalization of externalities engendered by the actions of controlling shareholders and yield underinvestment. Other frictions due to asymmetric information, or free rider and empty core problems in the bargaining/recontracting process, can also undermine Coaseian efficiency and generate underinvestment equilibria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.231
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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